Combining Smart Factors Momentum and Market Portfolio

1from AlgoLib import *
2import numpy as np
3#endregion
4
5class CombiningSmartFactorsMomentumandMarketPortfolio(XXX):
6
7def Initialize(self):
8self.SetStartDate(2000, 1, 1)
9self.SetCash(100000)
10
11self.symbols = {
12    'momentum' : 'US_EQUAL_DECILE_1500_12_2m_L_S',
13    'value' : 'US_EQUAL_DECILE_1500_B_M_L_S',
14    'quality' : 'US_EQUAL_DECILE_1500_ROA_L_S',
15    'size' : 'US_EQUAL_DECILE_1500_Size_L_S',
16    'volatility' : 'US_EQUAL_DECILE_1500_Volatility_L_S',
17    }
18
19# monthly price data
20self.data = {}
21self.long_period = 13
22self.short_period = 2
23self.max_missing_days:int = 5
24
25self.monthly_returns = {}
26self.monthly_returns_period = 12
27
28for symbol, equity_symbol in self.symbols.items():
29    data = self.AddData(USEquity, equity_symbol, Resolution.Daily)
30    data.SetLeverage(10)
31    data.SetFeeModel(CustomFeeModel())
32    self.data[symbol] = RollingWindow[float](self.long_period)
33    
34self.market = self.AddEquity("IWM", Resolution.Daily).Symbol
35self.data[self.market] = RollingWindow[float](self.short_period)
36
37self.monthly_returns['smart_factors'] = RollingWindow[float](self.monthly_returns_period)
38self.monthly_returns['market'] = RollingWindow[float](self.monthly_returns_period)
39
40self.recent_month:int = -1
41
42def OnSecuritiesChanged(self, changes):
43for security in changes.AddedSecurities:
44    security.SetFeeModel(CustomFeeModel())
45    security.SetLeverage(5)
46    
47def OnData(self, data):
48# store factor monthly prices
49for symbol, equity_symbol in self.symbols.items():
50    if equity_symbol in data and data[equity_symbol]:
51        price = data[equity_symbol].Value
52        self.data[symbol].Add(price)
53
54# store market prices
55if self.market in data and data[self.market]:
56    market_price = data[self.market].Value
57    self.data[self.market].Add(market_price)
58
59if self.recent_month == self.Time.month:
60    return
61self.recent_month = self.Time.month
62    
63slow_momentum = {}
64fast_momentum = {}
65
66# calculate both momentum values
67for symbol, equity_symbol in self.symbols.items():
68    if self.Securities[equity_symbol].GetLastData() and (self.Time.date() - self.Securities[equity_symbol].GetLastData().Time.date()).days <= self.max_missing_days:
69        if self.data[symbol].IsReady:
70            slow_momentum[symbol] = self.data[symbol][0] / self.data[symbol][self.long_period-1] - 1
71            fast_momentum[symbol] = self.data[symbol][0] / self.data[symbol][1] - 1
72
73total_weight = {}
74if len(fast_momentum) != 0:
75    # momentum ranking
76    
77    # weights
78    rank_sum = sum([x for x in range(1, len(slow_momentum)+1)])
79    sorted_by_slow_momentum = sorted(slow_momentum.items(), key = lambda x: abs(x[1]), reverse = False)
80    slow_weight = {}
81    for i, (symbol, momentum) in enumerate(sorted_by_slow_momentum):
82        rank = i+1
83        slow_weight[symbol] = (rank / rank_sum) * np.sign(momentum)
84
85    sorted_by_fast_momentum = sorted(fast_momentum.items(), key = lambda x: abs(x[1]), reverse = False)
86    fast_weight = {}
87    for i, (symbol, momentum) in enumerate(sorted_by_fast_momentum):
88        rank = i+1
89        fast_weight[symbol] = (rank / rank_sum) * np.sign(momentum)
90    
91    # total weight
92    for symbol, equity_symbol in self.symbols.items():
93        if symbol in slow_momentum and symbol in fast_momentum:
94            s_weight = slow_weight[symbol]
95            f_weight = fast_weight[symbol]
96            total_weight[symbol] = 0.75*f_weight + 0.25*s_weight
97
98# retrun calculation for market and smart factors
99if self.data[self.market].IsReady:
100    market_return = self.data[self.market][0] / self.data[self.market][1] - 1
101    self.monthly_returns['market'].Add(market_return)
102    
103    # smart factor return calculation
104    smart_factors_return = 0
105    for symbol, momentum_1M in fast_momentum.items():
106        if symbol in total_weight:
107            w = total_weight[symbol]
108            symbol_ret = w*momentum_1M
109            smart_factors_return += symbol_ret
110            
111    if smart_factors_return != 0:
112        self.monthly_returns['smart_factors'].Add(smart_factors_return)
113    
114    score = {}
115    traded_weight = {}
116    
117    # calculate 12 SMA's
118    if self.monthly_returns['smart_factors'].IsReady and self.monthly_returns['market'].IsReady:
119        score['smart_factors'] = 0
120        score['market'] = 0
121        for sma_period in range(1, 13):
122            factor_returns = [x for x in self.monthly_returns['smart_factors']][:sma_period]
123            market_returns = [x for x in self.monthly_returns['market']][:sma_period]
124            
125            factor_mean_return = np.mean(factor_returns)
126            market_mean_return = np.mean(market_returns)
127            
128            if factor_mean_return > market_mean_return:
129                score['smart_factors'] += 1
130            else:
131                score['market'] += 1
132        
133        total_score = score['market'] + score['smart_factors']
134        if total_score != 0:
135            traded_weight['market'] = score['market'] / total_score
136            traded_weight['smart_factors'] = score['smart_factors'] / total_score
137            
138            # order execution
139            # market
140            self.SetHoldings(self.market, traded_weight['market'])
141            
142            # smart factors
143            for symbol, equity_symbol in self.symbols.items():
144                if symbol in total_weight:
145                    w = total_weight[symbol]
146                    self.SetHoldings(equity_symbol, traded_weight['smart_factors'] * w)
147                    
148class USEquity(PythonData):
149def GetSource(self, config, date, isLiveMode):
150return SubscriptionDataSource("data.quantpedia.com/backtesting_data/equity/us_ew_decile/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
151
152# File example.
153# date;equity
154# 1992-01-31;0.98
155def Reader(self, config, line, date, isLiveMode):
156data = USEquity()
157data.Symbol = config.Symbol
158
159if not line[0].isdigit(): return None
160split = line.split(';')
161
162# Prevent lookahead bias.
163data.Time = datetime.strptime(split[0], "%Y-%m-%d") + timedelta(days=1)
164data.Value = float(split[1])
165
166return data
167
168# Custom fee model
169class CustomFeeModel(FeeModel):
170def GetOrderFee(self, parameters):
171fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
172return OrderFee(CashAmount(fee, "USD"))